FastChat — Build Your Own Open-Source ChatGPT Clone with LLM Chatbots

LMSYS FastChat is the most comprehensive open-source platform for building, serving, and evaluating LLM-powered chatbots in 2026, offering tools for training instruction-tuned models like Vicuna and Alpaca, deploying production-grade conversational AI, and running benchmarks. The platform features a modular architecture with a distributed worker system for multi-GPU serving, an OpenAI-compatible API, and a web chat interface, enabling full transparency and reproducibility.

FastChat — Build Your Own Open-Source ChatGPT Clone with LLM Chatbots Complete guide to LMSYS FastChat, the open-source platform for training, serving, and evaluating large language model chatbots. Build production AI assistants with Vicuna, Alpaca, and more. - Updated 2026-07-17 TL;DR tldr LMSYS FastChat is the most comprehensive open-source platform for building, serving, and evaluating LLM-powered chatbots in 2026. From fine-tuning models like Vicuna and Alpaca to deploying production-grade conversational AI, this guide covers every aspect of the FastChat ecosystem. What Is FastChat? what-is-fastchat FastChat is an open platform developed by the Large Model System Organization LMSYS for training, serving, and evaluating large language model chatbots. It provides tools for creating instruction-tuned models, running benchmarks, and deploying chat interfaces — all while maintaining full transparency and reproducibility. Key Features key-features Model Training : Train instruction-tuned models from base checkpoints using RLHF or direct preference optimization Multiple Model Support : Out-of-the-box support for Vicuna, Alpaca, LLaMA, and custom architectures OpenAI-Compatible API : Drop-in replacement for OpenAI’s API with your own models Web Chat Interface : Beautiful, responsive UI for testing and deploying chatbots Evaluation Benchmarks : Built-in evaluation on MT-Bench, AlpacaEval, and other standard benchmarks Multi-GPU Serving : Efficient deployment across multiple GPUs with tensor parallelism Community Driven : Backed by LMSYS Org’s research community with thousands of contributors Architecture Overview architecture-overview FastChat follows a modular architecture: FastChat Models : Core model implementations supporting various architectures FastChat Serve : High-performance serving engine with OpenAI API compatibility FastChat Train : Training pipeline for instruction tuning and RLHF FastChat Eval : Evaluation framework for benchmarking model performance FastChat Data : Curated datasets for training and evaluation The Model Worker Architecture the-model-worker-architecture FastChat uses a distributed worker architecture where each model runs as an independent worker process: ┌─────────────┐ ┌──────────────┐ ┌─────────────────┐ │ Controller │────▶│ Model Worker│────▶│ Web UI / API │ │ port 21001 │ │ port 21002 │ │ port 7860 │ └─────────────┘ └──────────────┘ └─────────────────┘ │ │ │ ┌──────────────┐ │ │ Model Worker│ │ │ port 21003 │ │ └──────────────┘ │ ┌─────────────┐ │ Model Worker│ │ port 21004 │ └─────────────┘ The controller manages load balancing, health checks, and routing between workers. Multiple workers can serve different models or handle requests for the same model in parallel. Installation Guide installation-guide Quick Start with Docker quick-start-with-docker docker pull lmsysorg/fastchat:latest docker run -p 8000:8000 lmsysorg/fastchat:latest Manual Installation manual-installation git clone https://github.com/lm-sys/FastChat.git cd FastChat pip install -e ". model worker,webui " Install specific model dependencies: For LLaMA-based models pip install transformers accelerate For vLLM acceleration pip install vllm For TensorRT-LLM pip install tensorrt llm Verify Installation verify-installation python import fastchat print fastchat. version Test model loading from fastchat.model import load model model, tokenizer = load model "lmsys/vicuna-7b-v1.5", device="cuda" print "Model loaded successfully " Available Models available-models | Model | Parameters | Base Model | Best For | |---|---|---|---| | Vicuna-7B-v1.5 | 7B | LLaMA 2 | General conversation | | Vicuna-13B-v1.5 | 13B | LLaMA 2 | Complex reasoning | | Vicuna-33B-v1.5 | 33B | LLaMA 2 | Maximum capability | | Alpaca-7B | 7B | LLaMA | Instruction following | | Koala-13B | 13B | LLaMA | Academic tasks | | ChatGLM2-6B | 6B | GLM | Chinese language | | Baichuan2-7B | 7B | Baichuan | Chinese business | Loading Pre-trained Models loading-pre-trained-models python from fastchat.model import load model, get conversation template model, tokenizer = load model "lmsys/vicuna-7b-v1.5", device="cuda", num gpus=1, max gpu memory="22GiB" Create conversation template conv = get conversation template "vicuna" conv.append message conv.roles 0 , "Hello, who are you?" conv.append message conv.roles 1 , None Generate response state = model.chat conv, temperature=0.7, max new tokens=512 print state.messages -1 2 Building Your Own Chatbot building-your-own-chatbot Step 1: Prepare Training Data step-1-prepare-training-data Create instruction-response pairs: { "instruction": "Explain quantum computing in simple terms.", "input": "", "output": "Quantum computing uses quantum bits qubits that can exist in multiple states simultaneously..." }, { "instruction": "Write a Python function to calculate Fibonacci numbers.", "input": "", "output": "def fibonacci n :\n if n <= 1:\n return n\n return fibonacci n-1 + fibonacci n-2 " } Step 2: Fine-Tune with FastChat Train step-2-fine-tune-with-fastchat-train python -m fastchat.train.train \ --model name or path lmsys/vicuna-7b-v1.5 \ --data path ./training data.json \ --output dir ./my-finetuned-model \ --num train epochs 3 \ --per device train batch size 4 \ --gradient accumulation steps 8 \ --learning rate 2e-5 \ --fp16 True \ --save steps 100 \ --logging steps 10 \ --lr scheduler type cosine \ --warmup ratio 0.03 \ --weight decay 0.0 \ --max seq length 2048 Step 3: Evaluate Your Model step-3-evaluate-your-model python -m fastchat.eval.evaluate benchmark \ --model-path ./my-finetuned-model \ --benchmark mt bench \ --num-questions 800 Deploying to Production deploying-to-production OpenAI-Compatible API Server openai-compatible-api-server python from fastapi import FastAPI from pydantic import BaseModel from fastchat.serve.api provider import OpenAIAPIClient app = FastAPI class ChatRequest BaseModel : model: str messages: list temperature: float = 0.7 max tokens: int = 2048 @app.post "/v1/chat/completions" async def create chat completion request: ChatRequest : client = OpenAIAPIClient model name=request.model, temperature=request.temperature response = await client.chat completion messages=request.messages, max tokens=request.max tokens return response Start the server: python -m fastchat.serve.openai api server \ --model-path lmsys/vicuna-7b-v1.5 \ --host 0.0.0.0 \ --port 8000 Test with curl: curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "vicuna-7b", "messages": {"role": "user", "content": "Hello "} }' Multi-GPU Deployment multi-gpu-deployment For models larger than a single GPU can hold: python -m fastchat.serve.multi model worker \ --model-path lmsys/vicuna-33b-v1.5 \ --num-gpus 4 \ --worker-address http://worker1:21001 Configure model parallelism: python from fastchat.serve.model worker import ModelWorker worker = ModelWorker controller address="http://controller:21001", worker address="http://worker1:21002", model names= "vicuna-33b" , model path="lmsys/vicuna-33b-v1.5", num gpus=4 Kubernetes Deployment kubernetes-deployment apiVersion: apps/v1 kind: Deployment metadata: name: fastchat-service spec: replicas: 2 selector: matchLabels: app: fastchat template: spec: containers: - name: fastchat image: lmsysorg/fastchat:v1.0 command: "python", "-m", "fastchat.serve.openai api server" args: - "--model-path" - "lmsys/vicuna-13b-v1.5" - "--host" - "0.0.0.0" - "--port" - "8000" resources: limits: nvidia.com/gpu: 1 ports: - containerPort: 8000 Load Balancing with Multiple Workers load-balancing-with-multiple-workers Deploy multiple worker instances behind a load balancer: Worker 1 python -m fastchat.serve.model worker \ --controller-address http://controller:21001 \ --worker-address http://worker1:21002 \ --model-path lmsys/vicuna-7b-v1.5 \ --limit-worker-concurrency 16 Worker 2 python -m fastchat.serve.model worker \ --controller-address http://controller:21001 \ --worker-address http://worker2:21003 \ --model-path lmsys/vicuna-7b-v1.5 \ --limit-worker-concurrency 16 Web Chat Interface web-chat-interface Launch the Demo UI launch-the-demo-ui python -m fastchat.serve.webui --host 0.0.0.0 --port 7860 Access at http://localhost:7860 to interact with your deployed model through a beautiful web interface. Customizing the UI customizing-the-ui Modify fastchat/serve/gradio web server.py to customize: - Brand colors and logos - Available models list - Temperature and parameter controls - Conversation history management - Export functionality Embedding in External Applications embedding-in-external-applications Embed the chat interface in your existing application: <iframe src="http://your-fastchat-server:7860/embed" width="100%" height="600px" frameborder="0" </iframe Advanced Topics advanced-topics Reinforcement Learning from Human Feedback RLHF reinforcement-learning-from-human-feedback-rlhf Train models using human preferences: python -m fastchat.train.rlhf.train \ --model name or path lmsys/vicuna-7b-v1.5 \ --ref model path lmsys/vicuna-7b-v1.5 \ --data path ./human feedback data.json \ --output dir ./rlhf-finetuned-model \ --num train epochs 1 \ --learning rate 1e-5 Direct Preference Optimization DPO direct-preference-optimization-dpo Alternative to RLHF that directly optimizes policy from preference data: python -m fastchat.train.dpo.train \ --model name or path lmsys/vicuna-7b-v1.5 \ --data path ./preference data.json \ --output dir ./dpo-finetuned-model \ --num train epochs 3 \ --learning rate 1e-5 \ --per device train batch size 2 \ --gradient accumulation steps 8 Model Quantization for Edge Deployment model-quantization-for-edge-deployment Reduce model size for mobile or edge devices: Quantize to 4-bit python -m fastchat.model.quantize quantize \ --model-path lmsys/vicuna-7b-v1.5 \ --output-path ./vicuna-7b-q4 \ --bits 4 Load quantized model from fastchat.model import load model model, tokenizer = load model "./vicuna-7b-q4", device="cpu" Evaluating with MT-Bench evaluating-with-mt-bench Run the official MT-Bench evaluation: python -m fastchat.eval.evaluate mtbench \ --model-path ./my-finetuned-model \ --judge-model lmsys/vicuna-13b-v1.5 \ --output-file ./mtbench results.json Streaming Responses streaming-responses Enable streaming for real-time token generation: python from fastchat.serve.stream manager import StreamManager stream manager = StreamManager model=model, tokenizer=tokenizer, max new tokens=512, temperature=0.7 for token in stream manager.stream conv : print token, end="", flush=True Performance Comparison performance-comparison | Configuration | Tokens/sec | VRAM | Latency p99 | |---|---|---|---| | Vicuna-7B + CPU | 15 tok/s | N/A | 2.5s | | Vicuna-7B + RTX 3090 | 45 tok/s | 12 GB | 0.8s | | Vicuna-13B + 2x A100 | 30 tok/s | 48 GB | 1.2s | | Vicuna-33B + 4x A100 | 18 tok/s | 96 GB | 2.0s | | Vicuna-7B + vLLM | 80 tok/s | 8 GB | 0.4s | Integration Examples integration-examples ChatGPT Plugin Integration chatgpt-plugin-integration Use FastChat as a backend for ChatGPT plugins from fastchat.serve.api provider import OpenAIAPIClient client = OpenAIAPIClient model name="custom-vicuna" response = client.chat completion messages= {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is machine learning?"} LangChain Integration langchain-integration python from langchain.llms import HuggingFacePipeline from transformers import AutoTokenizer, AutoModelForCausalLM Load model tokenizer = AutoTokenizer.from pretrained "lmsys/vicuna-7b-v1.5" model = AutoModelForCausalLM.from pretrained "lmsys/vicuna-7b-v1.5", torch dtype=torch.float16, device map="auto" Create pipeline pipeline = HuggingFacePipeline model=model, tokenizer=tokenizer, max new tokens=512, temperature=0.7 Use with LangChain from langchain.chains import ConversationChain conversation = ConversationChain llm=pipeline response = conversation.predict input="Tell me about AI." RAG Retrieval-Augmented Generation Pipeline rag-retrieval-augmented-generation-pipeline Combine FastChat with vector databases for knowledge-grounded responses: python from langchain.vectorstores import FAISS from langchain.embeddings import HuggingFaceEmbeddings from langchain.chains import RetrievalQA Load vector store embeddings = HuggingFaceEmbeddings model name="sentence-transformers/all-MiniLM-L6-v2" vectorstore = FAISS.load local "./knowledge base", embeddings Create retriever retriever = vectorstore.as retriever search kwargs={"k": 5} Build RAG chain qa chain = RetrievalQA.from chain type llm=pipeline, chain type="stuff", retriever=retriever Query with context result = qa chain.run "What are the key features of this product?" Production Checklist production-checklist - Set up monitoring with Ray Dashboard - Configure autoscaling for variable workloads - Implement retry logic for transient failures - Use streaming responses for better UX - Monitor GPU utilization and memory usage - Set up alerts for cluster health - Document resource requirements for each deployment - Implement rate limiting for API endpoints - Add input sanitization to prevent prompt injection - Set up logging for audit trails FAQ faq Q1: How does FastChat compare to Ollama? q1-how-does-fastchat-compare-to-ollama FastChat provides more flexibility for custom model training and evaluation, while Ollama focuses on simplicity. FastChat supports more model architectures and offers better control over serving configurations. Q2: Can I use FastChat with non-Llama models? q2-can-i-use-fastchat-with-non-llama-models Yes, FastChat supports LLaMA, Mistral, Falcon, BLOOM, GPT-J, and many other architectures. The model registry includes dozens of pre-configured templates. Q3: What hardware do I need for production deployment? q3-what-hardware-do-i-need-for-production-deployment Minimum: 1x GPU with 12GB VRAM for 7B models. Recommended: 2x A100 40GB for 13B+ models. For high-throughput serving, consider vLLM or TensorRT-LLM acceleration. Q4: How do I handle concurrent users? q4-how-do-i-handle-concurrent-users Use FastChat’s built-in load balancing with multiple model workers. Each worker handles a portion of requests, and the controller distributes traffic automatically. Q5: Is FastChat suitable for commercial use? q5-is-fastchat-suitable-for-commercial-use Yes, FastChat is MIT licensed. However, check the licenses of individual models you train or serve, as some base models may have additional restrictions. Q6: How do I prevent prompt injection attacks? q6-how-do-i-prevent-prompt-injection-attacks Implement input filtering, use system prompts to define behavior boundaries, and consider adding a separate moderation model to scan user inputs before passing them to the main model. Q7: Can I add custom tools/function calling to FastChat? q7-can-i-add-custom-toolsfunction-calling-to-fastchat Yes, FastChat supports function calling through custom conversation templates. You can define tool schemas and let the model decide which tools to call based on user intent. Sources sources FastChat GitHub Repository https://github.com/lm-sys/FastChat LMSYS Org Research https://lmsys.org/ Vicuna Model Card https://lmsys.org/blog/2023-03-30-vicuna/ MT-Bench Evaluation https://huggingface.co/datasets/lmsys/mt bench FastChat Documentation https://github.com/lm-sys/FastChat/blob/main/docs/ Call to Action call-to-action Build your own AI assistant with FastChat’s open-source platform. Get started https://dibi8.com/auth/ with our deployment guides and model training tutorials.